{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0, 10, 20, 40, 60, 80, 100, 140, 180, 250, 300, 400, 500, 600]\n",
    "y = [0, 0, 0.2, 0.8, 2.0, 3.6, 5.4, 8.8, 11.8, 14.4, 16.6, 18.4, 19.2, 19.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f574a5600b8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 3, 4, 5, 8, 10, 15, 16.5, 20, 25, 30, 40, 50, 60, 70, 80]\n",
    "y = [0.46, 1.7, 3.7, 9.0, 19.0, 26.4, 36, 37.5, 33.5, 27.2, 21, 10.4, 5.1, 2.8, 1.1, 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5748606240>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 3, 4, 5, 8, 10, 15, 16.5, 20, 25, 30, 40, 50, 60, 70, 80]\n",
    "y = [0.46, 1.7, 3.7, 9.0, 19.0, 26.4, 45, 47.5, 59.9, 72.2, 80.9, 91.3, 96.4, 99.2, 100.3, 100.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5748bc8b70>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0, 10, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360,380, 400]\n",
    "y = [0, 0, 0.2, 0.8, 2.0, 3.6, 5.4, 7.4, 9.3, 11.0, 12.6, 13.6, 14.4, 15.1, 15.8, 16.4, 17.0, 17.4, 17.6, 17.8, 17.95, 18.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa239639828>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
